Literature DB >> 24657566

QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method.

Alla P Toropova1, Andrey A Toropov1, Jovana B Veselinović2, Filip N Miljković2, Aleksandar M Veselinović3.   

Abstract

A series of 107 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) with anti-HIV-1 activity as a non-nucleoside reverse transcriptase inhibitor (NNRTI) has been studied. Monte Carlo method has been used as a tool to build up the quantitative structure-activity relationships (QSAR) for anti-HIV-1 activity. The QSAR models were calculated with the representation of the molecular structure by simplified molecular input-line entry system and by the molecular graph. Three various splits into training and test set were examined. Statistical quality of all build models is very good. Best calculated model had following statistical parameters: for training set r(2) = 0.8818, q(2) = 0.8774 and r(2) = 0.9360, q(2) = 0.9243 for test set. Structural indicators (alerts) for increase and decrease of the IC50 are defined. Using defined structural alerts computer aided design of new potential anti-HIV-1 HEPT derivates is presented.
Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  CORAL software; Computer-aided drug design; HEPT; Monte Carlo method; QSAR; SMILES

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Year:  2014        PMID: 24657566     DOI: 10.1016/j.ejmech.2014.03.013

Source DB:  PubMed          Journal:  Eur J Med Chem        ISSN: 0223-5234            Impact factor:   6.514


  2 in total

1.  Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors.

Authors:  Emanuele Amata; Agostino Marrazzo; Maria Dichiara; Maria N Modica; Loredana Salerno; Orazio Prezzavento; Giovanni Nastasi; Antonio Rescifina; Giuseppe Romeo; Valeria Pittalà
Journal:  Data Brief       Date:  2017-09-21

2.  (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.

Authors:  Leonid A Stolbov; Dmitry S Druzhilovskiy; Dmitry A Filimonov; Marc C Nicklaus; Vladimir V Poroikov
Journal:  Molecules       Date:  2019-12-25       Impact factor: 4.411

  2 in total

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